Side Scan Sonar Image Compression

Abstract

Wireless communication between underwater vehicles such as side scan sonar (SSS) and its operator is crucial for perceiving correct and updated intelligence understanding of the seabed. This has many military applications such as underwater mine discovery, and civilian applications such as seabed texture analysis.

SSS images usually contain high resolution data, and have high frequency content. Hence, aren’t compressed well by simple compression schemes such as JPEG.
The Goal of this project is finding a compression algorithm that on the one hand manages to compress SSS with high compression ratios and low complexity, and on the other, preserve the images' features that has intelligence value.

Few compression schemes that specialize at high resolution image compression were examined, implemented and tested through the project. The best algorithm found, was compared to the JPEG 2000 standard as reference, by having subjective quality assessment tests, and comparing quality factors such as PSNR and SSIM.

Introduction

Communication between underwater vehicles and their operator is crucial for underwater communication. There are few underwater autonomic vehicles that manage to explore the seabed efficiently; one of them is the side scan sonar (SSS). SSS creates sonar mapping of the seabed and provides understanding of the differences in material and texture type of the seabed.

Data provided by the SSS, present large uniform areas disrupted by rocks, shipwrecks or pipelines and is inherently noisy. Therefore we must find effective ways for transmitting high resolution data should be found, to withstand the system’s limitation of bit rate and bandwidth.

Figure 2 - SSS image patch

The goal of the project is designing an efficient compression scheme for SSS images that preserve the image's important details, achieves high compression ratios and have low complexity.
The solution
Classic image compression algorithms such as JPEG don’t work well on SSS images for its high resolution data, and high frequency content.
The approach we chose was wavelet based compression. Wavelets are a family of functions, obtained from a prototype function by scaling and translating.

Wavelet representation allows fine frequency analysis and good localization in time. Wavelets are useful multi resolution signal analysis tool. They enable studying different resolution layers of the image and help de-noising some of the speckles of SSS images.

Figure 3 – Wavelet Decomposition

We implemented in matlab several wavelet based compression schemes. The first one is based on sparse representation of the wavelet coefficients and Huffman coding:implemented and tested through the project. The best algorithm found, was compared to the JPEG 2000 standard as reference, by having subjective quality assessment tests, and comparing quality factors such as PSNR and SSIM.

Figure 4 – Sparse DWT compression scheme

Second approach we implemented is based on SPIHT algorithm. The method uses the hierarchal structure of the wavelet decomposition and smart representation of the data to send a lot of information for a little cost. Information is sent in bit planes and represented in large hierarchal zero trees.

Figure 11 – MOS RankingConclusions
• Several SSS image compression and coding algorithms have been implemented, tested and compared.
• There is a tradeoff between high compression ratios to image quality.
• MOS Results were not conclusive.
• Choice of compression algorithm is highly influenced by the application.
Further Research
• Improving algorithm robustness for transmission errors.
• Implementing the algorithm in an operational system – real-time implementation.